As emerging pollutants, microplastics have attracted the attention of scholars from all over the world. However, there is a lack of research on freshwater areas, even in densely populated urban areas. This study investigated eight urban lakes in Changsha, China. It was found that microplastic concentrations ranged from 2425 ± 247.5 items/m3 to 7050 ± 1060.66 items/m3 in the surface water of research areas and the maximum concentration was found in Yuejin Lake, a tourist spot in the center of the city. Anthropogenic factors are an important reason for microplastic abundance in urban lakes. The major shape of microplastics was linear and most of the microplastics were transparent. More than 89.5% of the microplastics had a size of less than 2 mm. Polypropylene was the dominant type in the studied waters. This study can provide a valuable reference for a better understanding of microplastic pollution in urban areas of China.

Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.

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Ni-rich cathode (Ni > 0.8) provides a low-cost and high-energy-density solution to the next-generation lithium-ion batteries. Unfortunately, severe capacity fading of Ni-rich cathode caused by the interfacial and bulk structural degradation impeded its application. Herein, Zr doping and Li6Zr2O7 coating are applied to a Ni-rich LiNi0.83Co0.12Mn0.05O2 (NCM) layered cathode material, and the modified material exhibits excellent cycle stability. The 1%Zr-NCM cathode material maintains a discharge capacity of 173.9 mAh g–1 at 1 C after 200 cycles in the 2.5–4.3 V voltage range at 25 °C, corresponding to a capacity retention of 94.6%; however, the unmodified NCM only delivers 129.9 mAh g–1 (capacity retention 68.6%). The synergistic effect of bulk Zr doping and surface Li6Zr2O7 coating improves the cycle stability of the Ni-rich material. Zr doped into the bulk could form a strong Zr–O bond to stabilize the layered structure, and Zr located in the Li layer can act as a pillar to maintain the layered structure and reduce Li+/Ni2+ mixing. In addition, the Li6Zr2O7 coating layer can also play a dual role in promoting Li+ migration and suppressing surface side reactions. This work demonstrates that sufficiently utilizing zirconium to enhance the electrochemical performance of cathode materials is a feasible and promising strategy.

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We proposed a scheme to implement coherent population trapping (CPT) atomic clock based on the transient CPT phenomenon. We proved that the transient transmitted laser power in a typical Λ system near CPT resonance features as a damping oscillation. Also, the oscillating frequency is exactly equal to the frequency detuning from the atomic hyperfine splitting. Therefore, we can directly measure the frequency detuning and then compensated to the output frequency of microwave oscillator to get the standard frequency. By this method, we can further simplify the structure of CPT atomic clock, and make it easier to be digitized and miniaturized.